Pandas DataFrame 浮点格式
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Pandas DataFrame Float Formatting
提问by morningstar2651
I have a Pandas DataFrame of survey responses that I'm aggregating to averaged scores and outputting to HTML. My current code looks like this:
我有一个 Pandas DataFrame 的调查回复，我正在汇总平均分数并输出到 HTML。我当前的代码如下所示：
import pandas as pd
import numpy as np
df = pd.read_csv('survey_scores.csv', header=0)
np.round(pd.DataFrame(df.groupby('question_number').aggregate('mean').score).transpose(), 2).to_html()
It takes a DataFrame that looks kinda like this in csv...
它需要一个在 csv 中看起来有点像这样的 DataFrame ......
response_number, question_number, score
1, 1, 3.0
1, 2, 4.0
1, 3, 4.0
2, 1, 4.0
2, 2, 4.0
2, 3, 1.0
And it outputs the averaged scores to an HTML table that formats the score values like this:
它将平均分数输出到一个 HTML 表格，该表格将分数值设置为如下格式：
3.5, 4, 2.5
However, I'm trying to get the output to force each number to display two digits after the decimal point. I got it to round to two decimal points, but I'm having difficulty getting my output to format the values like this:
但是，我试图让输出强制每个数字在小数点后显示两位数。我把它四舍五入到两个小数点，但我很难让我的输出格式化这样的值：
3.50, 4.00, 2.50
How can I get these values formatted to two decimal places?
如何将这些值格式化为两位小数？
回答by morningstar2651
I've solved the problem. pandas.DataFrame.to_html()can format floats.
我已经解决了这个问题。pandas.DataFrame.to_html()可以格式化浮点数。
import pandas as pd
import numpy as np
df = pd.read_csv('survey_scores.csv', header=0)
np.round(pd.DataFrame(df.groupby('question_number').aggregate('mean').score).transpose(), 2).to_html(float_format=lambda x: '%.2f' % x)
回答by Alexander
df.groupby('question_number').aggregate('mean').score.transpose()\
.apply(lambda x: '{0:.2f}'.format(x))
Out[422]:
question_number
1 3.50
2 4.00
3 2.50
Name: score, dtype: object
回答by Bubai
Assuming you have floatvalues, format them to 2 digits.
假设您有float值，请将它们格式化为 2 位数字。
>>> print("%.2f" % 2.5)
2.50
